Geographic Variation in Cranial Morphology of Short-beaked Common Dolphins (Delphinus delphis) from the North Atlantic
Bibliographic record
Abstract
As part of an examination of the population structure of short-beaked common dolphins (Delphinus delphis) in the North Atlantic, I tested if there were systematic differences in cranial morphology, in relation to geographic location, for common dolphins both within the western North Atlantic (wNA; n = 141) and between the wNA and eastern North Atlantic (eNA; n = 106). Cranial specimens from the wNA were obtained between Nova Scotia, Canada, and Florida. Those from the eNA came from the Irish Sea and the coasts of Ireland and the United Kingdom. A Wilks' λ canonical discriminant analysis (CDA) was performed on the within-groups covariance matrix to test whether significant differences in group centroids (multivariate means) existed between putative population units separately for males and females. In addition, the CDA was used to reclassify each dolphin into a geographic group based on the discriminant function. The CDA of 35 cranial variables found no evidence (males: Wilks' λ = 0.603, P = 0.286; females: Wilks' λ = 0.145, P = 0.08) of population structure below the species level within the wNA. Thus, the 1-population model for this region was supported. CDAs revealed significant differences between the eNA and wNA for both males and females (males: Wilks' λ = 0.371, P < 0.0001; females: Wilks' λ = 0.260, P < 0.0001). Cross-validated reclassification rates for males were 78.8% (eNA) and 87.6% (wNA) and for females were 90.6% (eNA) and 81.4% (wNA). Measurements associated with the rostrum were important discriminating variables that might reflect differences in feeding habits between these areas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".